In many demand response programs, a customer is given an incentive to reduce electricity consumption in response to high prices or other conditions on the grid. To calculate these payments, the utility company has to estimate how much the ‘normal’ consumption for the customer would have been in the absence of a DR event. This estimated consumption is called the baseline.
The baseline is typically calculated using a mathematical formula such as the average consumption of a certain number of most recent ‘similar’ days which can be further adjusted by weather information. The DR day consumption is then compared to the actual usage to determine the credits a customer has earned for reducing the electricity consumption.
Since there is some amount of day-to-day or hour-to-hour variation in the normal consumption of a customer, the baseline value inherently is inaccurate. The daily variation in the consumption pattern is called the ‘noise’ in the baseline consumption.
Traditional baseline techniques have faced an important challenge with participation of small loads in demand response programs to determine the baseline and load-shed. A baseline can be inherently noisy; it can be very difficult to separate a small load-shed under the statistical noise of baseline energy consumption of the whole building meter.
The baseline computation techniques separate the load-shed signal from the random baseline noise.
The signal processing techniques can be enough to meet the rigorous requirements of the settlement of ISO/RTO managing the DR programs. In cases where the signal-to-noise power ratio (SNR) is very small and during the deployment of ultra-low power sensor with limited battery life, the problem of separating signal from ambient noise occurs.
The signal with low SNR can be recovered reliably in one of the three settings namely the signal with time domain (e.g. signal is sparse) or the signal with frequency domain (e.g. the wavelet coefficients have a tree like structure) or the signal with impact on a number of measurable quantities (e.g. multiple cameras can view the same object from slightly different angles).
Recently, Bayesian methods have also been applied to this estimation problem in order to provide prior knowledge of the environment and the signal.
The present invention relates to modern signal processing techniques that are able to decorrelate these signals and increase accuracy. Signal processing techniques are developing to detect small systematic load reduction in response to demand response price in relatively noise baseline environment.
Given that baseline usage can be inherently noisy, it can be hard to separate a small load-shed within the statistical error of baseline energy consumption model derived from the whole building meter.
By combining advanced signal processing techniques and the domain-specific engineering knowledge of the underlying data, DROMS-RT will allow separation of small systematic load sheds as per the stringent requirements of the settlement departments of the utilities or ISO/RTO managing the DR programs.